About this Abstract |
Meeting |
2021 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2021)
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Symposium
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Special Session
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Presentation Title |
Bayesian Process Optimization for Additively Manufactured Nitinol |
Author(s) |
Jiafeng Ye, Mohammad Salman Yasin, Jia Liu, Aleksandr Vinel, Daniel Silva Izquierdo, Nima Shamsaei, Shuai Shao |
On-Site Speaker (Planned) |
Jiafeng Ye |
Abstract Scope |
Nitinol, a popular shape memory alloy, has various applications in automotive, telecommunication, and medical. Additive manufacturing can further unleash its potential with great flexibility and cost-effectiveness in design and rapid prototyping. In this work, we utilize a Gaussian process-based Bayesian optimization method to efficiently optimize process parameters of the laser beam-powder bed fusion (LB-PBF) process to achieve high-density fabrication with nitinol shape memory alloy. Specifically, Gaussian process regression is applied to formulate a surrogate model between the critical process parameters (i.e., laser power, scanning speed) and the porosity of the nitinol samples. Then Bayesian optimization is integrated to successively explore the design space to find the optimal process parameters. Compared with the traditional trial-and-error methods, this method can quickly find the optimal process parameter for the high-quality nitinol products and accelerate the product innovation with nitinol in additive manufacturing. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |